MARP treats multi-agent emergent behavior as a regulation target, learning a shared reward model from episode-level outcomes

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Submitted 2026-08-07 (arXiv:2608.07280), Multi-Agent Reward Prediction inverts the usual posture toward emergence: instead of studying what multi-agent systems spontaneously do, it learns a shared reward model from episode-level evaluations of collective outcomes so decentralized agents align to a global social objective. In the Harvest Game — a canonical common-pool-resource social dilemma — MARP aligned behavior to sustainability, equality, and peace within one training framework, and high-level evaluation metrics could be changed without retraining. That retraining-free steerability is the practical hook for anyone running agent swarms with objectives that shift week to week.